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An Optimized Adaptive BP Neural Network Based on Improved lion swarm optimization algorithm and Its Applications

ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2024年第3期49卷 3417-3434页

作者： Liu, Miaomiao Zhang, Yuying Guo, Jingfeng Chen, Jing Northeast Petr Univ Sch Comp & Informat Technol Daqing 163318 Peoples R China Key Lab Petr Big Data & Intelligent Anal Heilongji Daqing 163318 Peoples R China Yanshan Univ Coll Informat Sci & Engn Qinhuangdao 066004 Peoples R China

An improved algorithm based on tent chaotic map and difference mechanism is proposed to address the problem of poor population diversity and easily falling into local optimum in the lion swarm optimization algorithm. At first, an improved tent chaotic map is introduced to initialize the population position to increase diversity and ergodicity, thereby improving the global search capability. Second, a perturbation factor combined with a differential evolution mechanism is introduced to realize the adaptive positioning of the lioness and improve its ability to jump out of the local optimum. Following that, the proposed algorithm's superiority is tested on 10 multi-type benchmark functions and compared to six swarm intelligence algorithms. Finally, the improved algorithm is applied to optimize the initial weights and thresholds of BP neural networks. Then a new model is proposed and applied to the study of house price prediction. Experiments are conducted on two standard datasets, and results show that the proposed model's convergence speed, accuracy, and stability are better than the other three methods. On the Boston dataset, the mean square error of the training set and test set is 0.0016 and 0.0052, respectively, and the absolute error of more than 98% of the data is within 3%. On the Californian dataset, the mean square error of the training set and test set is 0.0149 and 0.0151, respectively, with more than 92.3% of the data within 5% absolute error, further validating the effectiveness, higher accuracy, and convergence performance of the proposed model.

关键词： lion swarm optimization algorithm BP neural network Tent chaotic map Differential evolution

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Parallel Symbiotic lion swarm optimization algorithm Based on Latin Hypercube Distribution 13th

Parallel Symbiotic Lion Swarm Optimization Algorithm Based o...

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13th International Conference on swarm Intelligence (ICSI)

作者： Han, Zongxin Jiang, Mingyan Shandong Univ Sch Informat Sci & Engn Qingdao 266237 Peoples R China

ISBN: (纸本)9783031096778;9783031096761

In order to optimize the search process of lion swarm optimization algorithm, improve the accuracy of the result and save the time, we propose the parallel symbiotic lion swarm algorithm based on Latin Hypercube. First, Latin Hypercube is used to initial population. Secondly, the mutualistic symbiosis mechanism is proposed to increase the communications between agents. Then, reverse learning strategy of dimensional keyhole imaging is added to increase diversity of the optimization process. Finally, on the premise of ensuring the quality of the solution, the parallel computation is combined with the improved lion swarm optimization algorithm to improve the convergence rate. Several benchmark functions are used to evaluate the performance of the improved lion swarm algorithm, and the running time of the parallel algorithm is compared. Experimental results show that the efficiency, stability and convergence rate of the improved lion swarm algorithm are greatly improved.

关键词： lion swarm optimization algorithm Parallel computing Latin hypercube distribution Mutualism Dimensional keyhole imaging reverse learning

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Fuzzy Clustering algorithm Based on Improved lion swarm optimization algorithm 12th

Fuzzy Clustering Algorithm Based on Improved Lion Swarm Opti...

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12th International Conference on swarm Intelligence (ICSI)

作者： Yu, Haiyan Jiang, Mingyan Yuan, Dongfeng Xin, Miaomiao Shandong Univ Sch Informat Sci & Engn Qingdao 266237 Peoples R China Qilu Inst Technol Sch Mech & Elect Engn Jinan 250200 Peoples R China

ISBN: (纸本)9783030787424;9783030787431

Aiming at the shortcomings of fuzzy C-means (FCM) clustering algorithm that it is easy to fall into local minima and sensitive to initial values and noisy data, this paper proposes a fuzzy clustering algorithm based on improved lion swarm optimization algorithm. Aiming at the problem that lion swarm optimization (LSO) algorithm is easy to fall into the local optimum, this paper improves lion swarm optimization algorithm by introducing sin cos algorithm and elite opposition-based learning. In addition, the introduction of a supervision mechanism enhances the lions' ability to jump out of local optimum and improves the local search ability of lion swarm optimization algorithm. The optimal solution obtained by improved lion swarm optimization algorithm is used as the initial clustering center of FCM algorithm, then FCM algorithm is run to obtain the global optimal solution, which effectively overcomes the shortcomings of FCM algorithm. The experimental results show that, compared with original FCM clustering algorithm, FCM clustering algorithm based on improved lion swarm optimization algorithm has improved the algorithm's optimization ability and has better clustering results.

关键词： Fuzzy C-mean clustering lion swarm optimization algorithm Sin cos algorithm Data mining

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Multi-focus Image Fusion algorithm Based on Improved lion swarm optimization algorithm 4

Multi-focus Image Fusion Algorithm Based on Improved Lion Sw...

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4th International Conference on Computer, Control and Robotics (ICCCR)

作者： Jiang, Keqin Yuan, Dongfeng Zhou, Xiaotian Zhao, Ze Wang, Feng Jiang, Mingyan Shandong Univ Sch Informat Sci & Engn Qingdao Peoples R China Qilu Inst Technol Jinan Peoples R China Shandong Univ Sch Control Sci & Engn Jinan Peoples R China

ISBN: (纸本)9798350373141;9798350373158

Addressing the limitations of the lion swarm optimization (LSO) algorithm, such as its tendency to converge to local optima and its slow convergence rate, we propose an improved LSO algorithm that integrates Gaussian mapping and a somersault foraging strategy. Firstly, we advocate replacing the randomly generated initial population of the original algorithm with chaotic sequences generated via Gaussian mapping, thereby augmenting the diversity within the population. Secondly, the incorporation of the somersault foraging strategy is aimed at enhancing the diversity of optimization positions, thus bolstering the algorithm's resilience against local optima. Simulation experiments conducted on CEC2019 benchmark functions showcase notable enhancements in both convergence speed and solution accuracy with our proposed algorithm. Finally, the application of the improved LSO algorithm to multi-focus image fusion tasks reveals its superior performance in quantitative and visual assessments when compared against conventional techniques and genetic algorithm.

关键词： lion swarm optimization algorithm Gaussian mapping somersault foraging multi-focus image fusion

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An Adaptive lion swarm optimization algorithm Incorporating Tent Chaotic Search and Information Entropy

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INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS 2023年第1期16卷 39-39页

作者： Liu, Miaomiao Zhang, Yuying Guo, Jingfeng Chen, Jing Liu, Zhigang Northeast Petr Univ Sch Comp & Informat Technol Daqing 163318 Peoples R China Key Lab Petr Big Data & Intelligent Anal Heilongji Daqing 163318 Peoples R China Yanshan Univ Coll Informat Sci & Engn Qinhuangdao 066004 Peoples R China

This paper proposes an improved adaptive lion swarm optimization (LSO) algorithm integrating the chaotic search strategy and information entropy to address the problem that the standard LSO algorithm has slow convergence and easily falls into the local optimum in later iterations. At first, an adaptive factor is introduced to improve tent chaotic mapping and used for population position initialization to enhance population diversity and realize uniform traversal while ensuring random distribution, ultimately improving the global search ability. Second, to address the problem that the cub selection strategy is blind, resulting in insufficient traversal in the early stage, a dynamic step-size perturbation factor is established using the second-order norm and information entropy. Adaptive parameters are used to dynamically adjust the selection probability of different cub behaviors based on the number of iterations to suppress the premature convergence of the algorithm. Finally, tent chaotic search is employed to adaptively adjust the search range and improve the individuals with poor fitness through multiple neighborhood points of the local optimal solution, further improving the algorithm's search speed and accuracy. Experimental results on 18 benchmark functions revealed that the proposed algorithm yields superior performance in terms of convergence speed, optimization accuracy, and ability to jump out of the local optimal solution compared with the standard LSO, gray wolf optimizer, and particle swarm optimization algorithms. Furthermore, the improved LSO algorithm was used to optimize the initial weights and thresholds of the BP neural network, and the effectiveness of the proposed algorithm was further verified by studying the house price prediction problem using two real-world datasets.

关键词： lion swarm optimization algorithm Tent chaotic mapping Information entropy Adaptive parameter Tent chaotic search Second-order norm

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Improved lion swarm optimization algorithm to solve the multi-objective rescheduling of hybrid flowshop with limited buffer

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JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES 2024年第5期36卷

作者： Guan, Tingyu Wen, Tingxin Kou, Bencong Liaoning Tech Univ Sch Business Adm Huludao 125105 Peoples R China

As the realities of production and operation in green and intelligent workshops become more variable, the adverse risks arising from disruptions to modernized workshop energy consumption schedules and customer churn caused by dynamic events are increasing. In order to solve those problems, we take the intelligent hybrid flow shop as the research subject, use buffer capacity and automated guided vehicles (AGVs) transport devices as resource constraints, construct a multi-objective rescheduling model that considers both energy consumption and customer satisfaction. According to the model characteristics, an improved lion swarm optimization algorithm (ILSO) is designed to solve the above model. To improve the initial solution quality and global search capability of the algorithm, ILSO is improved by combining the reverse learning initialization strategy of Logistic chaotic mapping with the tabu search strategy. The results of experiments on the proposed algorithm with different sizes of arithmetic cases and real cases in the workshop indicate that ILSO can effectively solve the bi-objective rescheduling problem oriented to inserting orders, and the proposed model can provide green dynamic scheduling solutions for manufacturing enterprises to achieve the purpose of transformation to green intelligent manufacturing.

关键词： Rescheduling Energy consumption Satisfaction lion swarm optimization algorithm Limited buffer

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A Hybrid Improved lion swarm optimization algorithm 23

A Hybrid Improved Lion Swarm Optimization Algorithm

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Proceedings of the 2023 2nd International Symposium on Computing and Artificial Intelligence

作者： Keqin Jiang Mingyan Jiang School of information science and engineering Shandong university China

ISBN: (纸本)9798400708954

Abstract: When solving multimodal optimization problems, the lion swarm optimization (LSO) algorithm will face many problems, such as low individual diversity, slow search speed, premature convergence or even falling into local extremum. The traditional approach is to introduce chaotic search, gaussian mutation or other mutation strategies to enhance the local search ability of the LSO algorithm. These improvements have been verified and the performance of the algorithm can be improved to a certain extent. However, these improved strategies lack effective use of population information, which will still affect the performance of the algorithm in search speed and search accuracy. To solve this problem, on the basis of the above-mentioned improved algorithm, this paper introduces the distribution estimation algorithm and proposes a hybrid improved LSO algorithm. The hybrid improved algorithm analyzes and learns the structure of the problem by constructing a probability model for the dominant group, and guides the efficient optimization of individuals in the population according to this information. It is verified in five standard test functions and ten test functions of IEEE CEC 2021. Compared with the traditional improved LSO algorithm, the results show that the hybrid improved LSO algorithm is superior.

关键词： Chaotic search Distribution estimation algorithm lion swarm optimization algorithm Mutation strategy

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Bi-objective lion swarm optimization based on teaching and learning algorithm 2022

Bi-objective lion swarm optimization based on teaching and l...

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6th International Conference on Machine Learning and Soft Computing (ICMLSC)

作者： Zhang, Qi Jiang, Mingyan Shandong Univ Sch Informat Sci & Engn Qingdao 266237 Peoples R China

ISBN: (纸本)9781450387477

To address the problem that it is difficult to obtain a good quality and uniformly distributed Pareto optimal solution set for a complex biobjective system model, this paper proposes a Teaching-Learning-based Bi-objective lion swarm optimization algorithm (TLBLSO) for solving the bi-objective system uniform Pareto solution set problem. The idea and mechanism of teaching and learning optimization algorithm are introduced into the lion swarm algorithm. That is, the knowledge level of the whole group is improved by the way of individual teachers imparting knowledge and students exchanging knowledge, which effectively improves the spatial searching ability of the lion swarm. By comparing with other optimization algorithms, the experimental results show that the proposed TLBLSO can obtain well-distributed optimization solutions, which verifies the superiority of the proposed algorithm in this paper.

关键词： teaching and learning bi-objective lion swarm optimization algorithm uniform distribution

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Weighted sum rate maximization for smart reflective surfaces based on improved lion swarm algorithm

Weighted sum rate maximization for smart reflective surfaces...

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International Conference on Ubiquitous Communication (Ucom)

作者： Wang, Feng Jiang, Mingyan Jiang, Keqin Zhao, Ze Shandong Univ Sch Informat Sci & Engn Qingdao 266237 Peoples R China

ISBN: (纸本)9798350374223;9798350374216

With the maturation and widespread adoption of 5G communication network technology, research into next-generation (6G) communication technology has intensified to meet the demands of data transmission in the big data era. Reflective Intelligent Surface (RIS) technology has emerged as a promising approach for advancing 6G communication capabilities. The objective of employing intelligent reflective surfaces in wireless network transmission is to optimize beamforming at the base station transmitter and the reflection coefficient of the RIS. This optimization aims to maximize the weighted sum-rate (WSR) at the user side, while adhering to constraints related to base station transmitter power and RIS unit modes. In this paper, we propose enhancements to the basic lion swarm optimization algorithm by incorporating elements such as a good point set, chaotic search, and a mining mechanism inspired by the honey badger algorithm. These modifications are intended to improve the algorithm's performance in optimizing the aforementioned parameters. Simulation results demonstrate that our enhanced lion swarm optimization algorithm achieves significant improvements in weighting sum rate maximization, thereby enhancing the efficiency of reflective intelligent surface-assisted wireless networks.

关键词： lion swarm optimization algorithm intelligent reflective surfaces the weighted sum rate good point set random weight update honey badger algorithm

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LSO-FastSLAM: A New algorithm to Improve the Accuracy of Localization and Mapping for Rescue Robots

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SENSORS 2022年第3期22卷 1297-1297页

作者： Zhu, Daixian Ma, Yinan Wang, Mingbo Yang, Jing Yin, Yichen Liu, Shulin Xian Univ Sci & Technol Coll Commun & Informat Engn Xian 710054 Peoples R China Xian Univ Sci & Technol Coll Energy Sci & Engn Xian 710054 Peoples R China Xian Xiangteng Microelect Technol Co Ltd Xian 710054 Peoples R China Xian Univ Sci & Technol Coll Elect & Control Engn Xian 710054 Peoples R China

This paper improves the accuracy of a mine robot's positioning and mapping for rapid rescue. Specifically, we improved the FastSLAM algorithm inspired by the lion swarm optimization method. Through the division of labor between different individuals in the lion swarm optimization algorithm, the optimized particle set distribution after importance sampling in the FastSLAM algorithm is realized. The particles are distributed in a high likelihood area, thereby solving the problem of particle weight degradation. Meanwhile, the diversity of particles is increased since the foraging methods between individuals in the lion swarm algorithm are different so that improving the accuracy of the robot's positioning and mapping. The experimental results confirmed the improvement of the algorithm and the accuracy of the robot.

关键词： mine robot simultaneous localization and mapping FastSLAM lion swarm optimization algorithm particle weight particle diversity

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